Graph-based segmentation with homogeneous hue and texture vertices

Lua Ngo, Jae Ho Han

Research output: Contribution to journalArticlepeer-review

Abstract

This work presents an automated segmentation method, based on graph theory, which processes superpixels that exhibit spatially similarities in hue and texture pixel groups, rather than individual pixels. The graph shortest path includes a chain of neighboring superpixels which have minimal intensity changes. This method reduces graphics computational complexity because it provides large decreases in the number of vertices as the superpixel size increases. For the starting vertex prediction, the boundary pixel in first column which is included in this starting vertex is predicted by a trained deep neural network formulated as a regression task. By formulating the problem as a regression scheme, the computational burden is decreased in comparison with classifying each pixel in the entire image. This feasibility approach, when applied as a preliminary study in electron microscopy and optical coherence tomography images, demonstrated high measures of accuracy: 0.9670 for the electron microscopy image and 0.9930 for vitreous/nerve-fiber and inner-segment/ outer-segment layer segmentations in the optical coherence tomography image.

Original languageEnglish
Pages (from-to)541-549
Number of pages9
JournalOptica Applicata
Volume51
Issue number4
DOIs
Publication statusPublished - 2021

Bibliographical note

Funding Information:
Acknowledgements – This work was supported in part by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2016-0-00464) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation). This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2020R1A4A1018309). J.-H. Han was also supported in part by Korea University Future Research Grant (FRG).

Publisher Copyright:
© 2021 WrocÅ‚aw University of Science and Technology. All rights reserved.

Keywords

  • Deep neural network
  • Electron microscopy
  • Image segmentation
  • Optical coherence tomography
  • Pattern recognition

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics

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